Researches and develops computer vision and sensing technologies including 3D vision, image analysis, biometrics, sensing, and machine learning for autonomous vehicles, manufacturing, factory automation, and bio-medical imaging.
Core technology areas include object recognition, event detection and recognition, tracking, 3D scene modeling, multimedia big-data analysis, computational imaging, and data fusion techniques.
Real-world applications include human machine interfaces, unmanned aerial vehicles, cross-border monitoring and safety, security and surveillance, vision-guided robotics, and intelligent transportation systems
Possesses expertise in:
- 3D Vision Algorithms and Systems – use multiple cameras or depth sensors for high resolution data acquisition to achieve a 3D map of the environment and or object and increase tracking efficiency (in comparison to 2D systems); target applications include 3D face recognition, road awareness, and factory automation
- Novel Vision Techniques and Sensor Fusion for Safety Applications - advanced vision technologies for safety- related applications such as hidden weapon detection and identifying potential driving hazards
- Miniaturized Stereo-Vision System – using a state-of-the-art embedded processor technology, developed a fully integrated and operational system that targets applications requiring power efficiency, compactness, and low cost; capable of inferring 3-D geometry of the scene in real time
- Machine Learning Algorithms – Focus on classification of patterns, dimension reduction of high dimensional input data to low dimensional output feature space, and sparse learning for image reconstruction from low dimensional features; algorithms can also be used for efficient hybrid system approximation
- Brain MRI Segmentation - Computer algorithms for the delineation of anatomical structures and other regions of interest to assist and automate specific radiological tasks
- Algorithms and Hardware Prototyping for Human Action Recognition – algorithms for real-time recognition of human activities using available advanced features in FPGAs
To get involved with the Computer Vision and Sensing Systems Lab
Contact: Dr. Jonathan Wu
Department of Electrical and Computer Engineering
519-253-3000, Ext. 2580
Visit the Computer Vision Lab website for more information.